1. Field of the Invention
The present invention relates generally to an image processing method and an image processing device, and more specifically, to a method and device for reducing image color noise captured by an image sensor.
2. Description of the Related Art
In general, a digital camera or a camcorder uses an image sensor, such as a Charge Coupled Device (CCD), a Complementary Metal Oxide Semiconductor (CMOS), or the like, instead of using film. The CCD is classified into a multiple CCD and a single CCD depending to the number of colors focused on a pixel. A multiple CCD can provide more accurate luminance and more accurate matching with a primary color for each pixel, as compared to a single CCD. However, the multiple CCD uses at least three times as many sensors as those used in the single CCD in order to detect each color component according to used color formats, such that the hardware architecture becomes complex and increases hardware size. For this reason, the single CCD has been predominantly used rather than the multiple CCD.
In the case of the single CCD, each pixel stores only one color information of the RGB color channels. Therefore, in order to obtain the complete information on images, the color information of other color channels that is not stored in the pixel should be interpolated from pixel information adjacent to the pixel. However, when unwanted information is interpolated during the interpolation process, noise or artifacts, which are largely unpleasant to the eye, are generated in images.
Therefore, in order to reduce the noise, research has been conducted in the field of image processing. A noise reducing algorithm may be classified into a method using a restoration mechanism, a method using a filtering mechanism, and the like. Since the restoration mechanism depends on accurate modeling for noise, excellent results are obtained but the burden on hardware is increased. Therefore, a method of using local probabilistic characteristics, for example, Local Linear Minimum Mean Square Error (LLMMSE), or the like, has been primarily used. A relatively simple bilateral filter approximating the LLMMSE, and the like, has been primarily used. The following Equation (1) is a general type of the LLMMSE:
            out      ⁡              [        r        ]              ⁡          [      c      ]        =                    mean        ⁡                  [          r          ]                    ⁡              [        c        ]              +                            var          ⁡                      (                                          in                ⁡                                  [                  r                  ]                                            ⁡                              [                c                ]                                      )                                    var_noise          +                      var            ⁡                          (                                                in                  ⁡                                      [                    r                    ]                                                  ⁡                                  [                  c                  ]                                            )                                          ⁢              (                                            in              ⁡                              [                r                ]                                      ⁡                          [              c              ]                                -                                    mean              ⁡                              [                r                ]                                      ⁡                          [              c              ]                                      )            where mean [r][c] represents the mean of (r,c) points, var (in[r][c]) represents variance of r, c points, and var_noise represents the variance of noise. In Equation (1), when the variance (var_noise) of noise is relatively larger than variance (var(in[r][c]) of signal, var (in[r][c])/(var_noise+var(in[r][c]) approximates “0” and the output out[r][c] approximates the mean accordingly, such that noise is reduced. On the other hand, when the variance of a signal is larger than the variance of a noise as in an edge region, var(in[r][c])/(var_noise+var(in[r][c])) approximates “1” and an output (out[r][c]) approximates an original signal in [r][c] accordingly, such that noise is less reduced.
An example of other color noise reducing filters may include a Mean Filter (MF), a Vector Median Filter (VMF), a Vector Directional Filter (VDF), and the like.
FIG. 1 is a diagram illustrating an example of MF, VMF, and VDF according to the related art. The MF depends on a method of obtaining a mean of pixel values in a local region. In FIG. 1, the MF result for three pixels having different directions and phases is
  MF  =            (                                                  R              1                        +                          R              2                        +                          R              3                                3                ,                                            G              1                        +                          G              2                        +                          G              3                                3                ,                                            B              1                        +                          B              2                        +                          B              3                                3                    )        .  
However, since the MF is a Low Pass Filter (LPF), in addition to noise, high frequency components necessary for images, such as an edge, is also reduced, such that the detail of images is reduced.
The median filter is a filter, which is efficient in reducing laplacian noise, which can efficiently reduce pixels in which colors are visually splashed. The VMF, which is a median filter, outputs a vector having an intermediate magnitude among color vectors in a local region as a result. For example, referring to FIG. 1, the VMF outputs a color value corresponding to {right arrow over (v)}3 having an intermediate magnitude among color vectors {right arrow over (v)}1, {right arrow over (v)}2, {right arrow over (v)}3 representing three pixels as a result. That is, the VMF outputs VMF=(R3, G3, B3) as a result.
The VDF is a filter that outputs a color vector having an intermediate phase among the color vectors in a local region as a result. For example, referring to FIG. 1, the VMF outputs a color value corresponding to {right arrow over (V)}2 having an intermediate phase among color vectors {right arrow over (v)}1, {right arrow over (v)}2, {right arrow over (v)}3 representing three pixels as a result. That is, the VMF outputs VDF=(R2, G2, B2) as a result.
As described above, the methods for reducing color noise, such as MF, VMF, VDF, or the like, according to the related art, uniformly reduce the color noise to the same degree without accurately considering the correlation between luminance Y of images and chrominance Cb and Cr of images and edge characteristics.